from __future__ import annotations

from collections import Counter
from random import random
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      {}







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      {}







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						float				):





    """simple docstring"""


    if nodea not in self.connections:
     self.add_node(lowerCamelCase_				)
    if nodea not in self.connections:
     self.add_node(lowerCamelCase_				)
    UpperCamelCase			  =      probability







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    return list(self.connections				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      0
    UpperCamelCase			  =      random()

    for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
      return dest
    return ""





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	dict[str, int]:







 '''simple docstring'''
 UpperCamelCase			  =      MarkovChainGraphUndirectedUnweighted()

 for nodea, nodea, probability in transitions:
  graph.add_transition_probability(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 UpperCamelCase			  =      Counter(graph.get_nodes()       )
 UpperCamelCase			  =      start

 for _ in range(UpperCamelCase_       ):
  UpperCamelCase			  =      graph.transition(UpperCamelCase_       )
  visited[node] += 1

 return visited


if __name__ == "__main__":
     import doctest

     doctest.testmod()

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
